Principal AI Engineer

Enterprise Solutions Inc.; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Enterprise Solutions Inc. seeks experienced software engineers to build large‑scale Python systems and agentic AI platforms. The role requires hands‑on Python and at least one systems language (Go/Rust/Java/C/C++) with strong data structures and system design skills, plus API/microservice experience and production data pipelines.

You will work across cloud deployments (AWS), implement guardrails for LLMs, and mentor teammates while delivering production‑grade AI products.

Qualifications

• 8–14 years of software engineering experience with hands-on large-scale Python.
• Depth in at least one systems/backend language (Go, Rust, Java, or C/C++).
• Strong data structures, algorithms, APIs, microservices and system design.
• Hands-on experience with data pipelines and distributed systems.
• 2+ years of hands-on LLM engineering and production agent frameworks.
• Experience building MCP servers, tool-calling interfaces, and RAG systems.
• Familiarity with vector databases and guardrails for reliable AI apps.
• Token optimization, context window management, and latency/cost tuning.
• Experience designing evaluation frameworks and instrumentation for enterprise.
• Hands-on AWS (ECS/EKS, Lambda, S3, DynamoDB, Redshift) and IaC (Terraform/CloudFormation).
• CI/CD practices and cloud integration; cross-functional collaboration; mentoring.

Skills

Python

Go

Rust

Java

C/C++

Data Structures

Algorithms

APIs

Microservices

System Design

LLM Engineering

Agent Frameworks

LangGraph

Google ADK

CrewAI

Claude Agent SDK

MCP Servers

RAG

Vector Databases

Milvus

Pinecone

Weaviate

FAISS

Guardrails

Reliability

Token Optimization

AWS

CI/CD

Terraform

CloudFormation

Cross-functional

Mentoring

Python SQL

Tools

Milvus

Pinecone

Weaviate

FAISS

LangSmith

Langfuse

Terraform

CloudFormation

AWS

ECS

EKS

S3

DynamoDB

Redshift

Step Functions

Job description

Employment Type: Full Time (Overlapping EST)

Experience Level: Staff/Principal (8–14 years)

What We're Looking For

• 8–14 years of software engineering experience, with strong hands‑on large‑scale Python
• Working depth in at least one systems or backend language — Go, Rust, Java, or C/C++ — and the judgment to know when to reach for it
• Strong data structures and algorithms.
• Strong understanding of APIs, microservices, and system design
• Hands‑on experience building and operating data pipelines and production‑grade distributed systems.

Agentic AI and LLMs

• 2+ years of hands‑on LLM engineering, with at least couple agentic system you designed and took to production
• Production experience with agent frameworks — LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent — and the fluency to move between them as the ecosystem evolves
• Experience building MCP (Model Context Protocol) servers and tool‑calling interfaces
• RAG from first principles: chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation
• Strong experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc. or cloud equivalents)
• Design of guardrails and reliability patterns — validators, policy checks, self‑correction loops, deterministic fallbacks, circuit breakers, and rollback paths

Optimization

• Deep familiarity with token optimization and context‑window management — context shaping, pruning, and compaction
• Latency and cost optimization through caching, model routing, batching, streaming, and parallel tool calls
• Performance testing and tuning systems against defined SLOs

Evaluation

• Experience building evaluation frameworks for LLM systems — offline eval sets, continuous online evaluation, and regression detection
• Instrumentation and traceability suitable for regulated enterprise environments using tools like LangSmith, Langfuse, etc.

Cloud

• Hands‑on AWS: containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift) and orchestration (Step Functions); Azure or GCP equivalents also valued
• Familiarity with CI/CD pipelines and DevOps practices
• Infrastructure as code with Terraform or CloudFormation, and mature CI/CD practice

Working traits

• Strong analytical problem‑solving with a bias to ownership and urgency
• Clear cross‑team communication, working directly with client stakeholders to translate business problems into technical roadmaps
• Able to work productively in ambiguity from system‑level documentation and ramp quickly in unfamiliar codebases

Good to Have

• Design and build agentic systems: Lead the architecture and implementation of tool‑calling agents that combine retrieval, structured reasoning, and secure action execution with least‑privilege access.
• Productionize LLM applications: Build retrieval pipelines, prompt synthesis, response validation, and self‑correction loops, backed by rigorous evaluation.
• Own the full stack: Deliver the data pipelines, backend services, distributed compute, and orchestration layer that agentic systems depend on — not only the model invocation.
• Engineer for reliability and governance: Build validator models, adversarial test suites, and policy checks; enforce deterministic fallbacks and rollback strategies; instrument continuous evaluation.
• Optimize for cost and latency: Drive measurable improvements in token efficiency, response time, and unit economics against defined SLOs.
• Codebase ownership: Build, maintain, and review high‑quality Python and SQL, with an emphasis on reusable components, scalability, and performance.
• Cloud integration: Deploy AI applications on AWS, Azure, or GCP with optimized resource usage and robust CI/CD.
• Cross‑functional collaboration: Partner with product owners, data scientists, and business SMEs to define requirements and deliver impactful AI products.
• Mentoring and technical leadership: Set engineering standards and share knowledge across the team, raising the bar on AI and software engineering practice.